Gen AI Engineer

LLM/Prompt-Context Engineer – Fullstack Python (AI Agents, LangGraph, Context Engineering) Position Overview: We are looking for a highly skilled LLM/Prompt-Context Engineer with a strong fullstack Python background to design, develop, and integrate intelligent systems focused on large language models (LLMs), prompt engineering, and advanced context management. In this role, you will play a critical part in architecting context-rich AI solutions, crafting effective prompts, and ensuring seamless agent interactions using frameworks like LangGraph. Key Responsibilities: Prompt & Context Engineering: Design, optimize, and evaluate prompts for LLMs to achieve precise, reliable, and contextually relevant outputs across a variety of use cases. Context Management: Architect and implement dynamic context management strategies, including session memory, retrieval-augmented generation, and user personalization, to enhance agent performance. LLM Integration: Integrate, fine-tune, and orchestrate LLMs within Python-based applications, leveraging APIs and custom pipelines for scalable deployment. LangGraph & Agent Flows: Build and manage complex conversational and agent workflows using the LangGraph framework to support multi-agent or multi-step solutions. Fullstack Development: Develop robust backend services, APIs, and (optionally) front-end interfaces to enable end-to-end AI-powered applications. Collaboration: Work closely with product, data science, and engineering teams to define requirements, run prompt experiments, and iterate quickly on solutions. Evaluation & Optimization: Implement testing, monitoring, and evaluation pipelines to continuously improve prompt effectiveness and context handling. Required Skills & Qualifications: Deep experience with fullstack Python development (FastAPI, Flask, Django; SQL/NoSQL databases). Demonstrated expertise in prompt engineering for LLMs (e.g., OpenAI, Anthropic, open-source LLMs). Strong understanding of context engineering, including session management, vector search, and knowledge retrieval strategies. Hands-on experience integrating AI agents and LLMs into production systems. Proficient with conversational flow frameworks such as LangGraph. Familiarity with cloud infrastructure, containerization (Docker), and CI/CD practices. Exceptional analytical, problem-solving, and communication skills. Preferred: Experience evaluating and fine-tuning LLMs or working with RAG architectures. Background in information retrieval, search, or knowledge management systems. Contributions to open-source LLM, agent, or prompt engineering projects. Job Types: Full-time, Contract Pay: $55.92 - $60.30 per hour Expected hours: 40 per week Experience: AI Agents: 5 years (Required) LangGraph: 5 years (Required) Context Engineering: 3 years (Required) Python: 8 years (Required) Location: Dallas, TX 75201 (Required) Ability to Commute: Dallas, TX 75201 (Required) Ability to Relocate: Dallas, TX 75201: Relocate before starting work (Required) Work Location: In person